Employing observational method for prospective data collection: A case study for analyzing diagnostic process and evaluating efficacy of TCM treatments for diabetes mellitus

Abstract

Ethnopharmacological relevanceWith the mounting pandemic of glucose metabolism dysregulation and type 2 Diabetes Mellitus (T2DM), traditional medicine such as traditional Chinese medicine (TCM) recipes has been widely adopted as a part of therapeutic approach, especially in Asian countries. Aim of the studyA novel approach, which is adopted from cohort studies from epidemiology has been applied to explore the clinical efficacy, as well as the herbal component selection of a variety of TCM formulations against T2DM. Materials and methodsIn the current study, 98 newly diagnosed T2DM patients were recruited in two hospitals. Over a span of 4 weeks, the patients were treated by prescriptions of their individual TCM physicians. General TCM symptoms, blood glucose parameters, as well as general metabolic health biomarkers were evaluated over the therapy period. The pattern of which herbs were used, together with association between blood glucose level change and the use of herbs, were analyzed. ResultsTCM diabetic syndrome diagnosis was made by physicians based on symptoms, who prescribed herbal TCM medication afterwards for individual subjects. The results showed significant reduction in fasting and postmeal glucose levels, as well as insulin after the TCM treatment regimen as compared to baseline. As secondary endpoint, total triglyceride level decreased over the period of study as well. Kudzuvine root, Rhemannia root, Figwoot root, and Mulberry leaf were the top herbs associated with pronounced glucose reduction. ConclusionsIn conclusion, an observational study on a cohort of patients receiving TCM therapy has shown good clinical outcome for T2DM patients receiving TCM treatments. Association analysis on herbal usage and clinical outcome suggested opportunity in constructing optimized formulation for superior efficacy with future studies at a larger scale.